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Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
- Nur Yildirim,
- Hannah Richardson,
- Maria Teodora Wetscherek,
- Junaid Bajwa,
- Joseph Jacob,
- Mark Ames Pinnock,
- Stephen Harris,
- Daniel Coelho De Castro,
- Shruthi Bannur,
- Stephanie Hyland,
- Pratik Ghosh,
- Mercy Ranjit,
- Kenza Bouzid,
- Anton Schwaighofer,
- Fernando Pérez-García,
- Harshita Sharma,
- Ozan Oktay,
- Matthew Lungren,
- Javier Alvarez-Valle,
- Aditya Nori,
- Anja Thieme
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing SystemsMay 2024, Article No.: 444, Pages 1–22https://doi.org/10.1145/3613904.3642013Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (...
- research-articleOctober 2023
Breast-Density Semantic Segmentation with Probability Scaling for BI-RADS Assessment using DeepLabV3
- Conrad T Testagrose,
- Vikash Gupta,
- Barbaros S Erdal,
- Richard D White,
- Robert W Maxwell,
- Xudong Liu,
- Indika Kahanda,
- Sherif Elfayoumy,
- William Klostermeyer,
- Mutlu Demirer
BCB '23: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health InformaticsSeptember 2023, Article No.: 25, Pages 1–6https://doi.org/10.1145/3584371.3612983Mammographic breast density is an early indicator of a patient's risk for breast cancer development. Although the direct cause is not fully understood, increased mammographic breast density increases the chance of developing breast cancer. Based on ...
- ArticleJune 2023
Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics
AbstractTo what extent can the patient’s length of stay in a hospital be predicted using only an X-ray image? We answer this question by comparing the performance of machine learning survival models on a novel multi-modal dataset created from 1235 images ...
- ArticleJune 2023
A General-Purpose AI Assistant Embedded in an Open-Source Radiology Information System
- Saptarshi Purkayastha,
- Rohan Isaac,
- Sharon Anthony,
- Shikhar Shukla,
- Elizabeth A. Krupinski,
- Joshua A. Danish,
- Judy Wawira Gichoya
Artificial Intelligence in MedicineJun 2023, Pages 373–377https://doi.org/10.1007/978-3-031-34344-5_46AbstractRadiology AI models have made significant progress in near-human performance or surpassing it. However, AI model’s partnership with human radiologist remains an unexplored challenge due to the lack of health information standards, contextual and ...
- surveyMarch 2023
Deep Learning Techniques for COVID-19 Diagnosis and Prognosis Based on Radiological Imaging
ACM Computing Surveys (CSUR), Volume 55, Issue 12Article No.: 260, Pages 1–39https://doi.org/10.1145/3576898This literature review summarizes the current deep learning methods developed by the medical imaging AI research community that have been focused on resolving lung imaging problems related to coronavirus disease 2019 (COVID-19). COVID-19 shares many of ...
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- short-paperMay 2022
Discussion Paper: The Integrity of Medical AI
WDC '22: Proceedings of the 1st Workshop on Security Implications of Deepfakes and CheapfakesMay 2022, Pages 31–33https://doi.org/10.1145/3494109.3527191Deep learning has proven itself to be an incredible asset to the medical community. However, with offensive AI, the technology can be turned against medical community; adversarial samples can be used to cause misdiagnosis and medical deepfakes can be ...
- short-paperJanuary 2022
Virtual Reality in Radiology: A Systematic Mapping Study
SVR '21: Proceedings of the 23rd Symposium on Virtual and Augmented RealityOctober 2021, Pages 177–181https://doi.org/10.1145/3488162.3488224In radiology, virtual reality has emerged as a candidate to solve some issues of the area, such as the ambient lighting and ergonomic postures when diagnosing.
The goal of this study is to explore the literature of virtual reality in radiology in order ...
- posterAugust 2021
Application of natural language processing and machine learning to radiology reports
BCB '21: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health InformaticsAugust 2021, Article No.: 67, Page 1https://doi.org/10.1145/3459930.3469496After radiologists perform a set of chest-x-rays (CXRs) they write a short report describing their observations and interpretations. Because these reports are free-text documents, there is the risk of miscommunication, which can result in reduced ...
- research-articleJuly 2021
Triage of 2D Mammographic Images Using Multi-view Multi-task Convolutional Neural Networks
ACM Transactions on Computing for Healthcare (HEALTH), Volume 2, Issue 3Article No.: 26, Pages 1–24https://doi.org/10.1145/3453166With an aging and growing population, the number of women receiving mammograms is increasing. However, existing techniques for autonomous diagnosis do not surpass a well-trained radiologist. Therefore, to reduce the number of mammograms that require ...
- research-articleApril 2021
CheXternal: generalization of deep learning models for chest X-ray interpretation to photos of chest X-rays and external clinical settings
CHIL '21: Proceedings of the Conference on Health, Inference, and LearningApril 2021, Pages 125–132https://doi.org/10.1145/3450439.3451876Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise. However, poor generalization due to data distribution shifts in clinical settings ...
- research-articleOctober 2019
Phenomenological ethnography can lead to the improvement of radiology diagnostics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems (SAGE-ADAP), Volume 27, Issue 5Oct 2019, Pages 347–350https://doi.org/10.1177/1059712319861663This opinion piece presents insights derived from research conducted in a radiology department in the United States. For several weeks I followed the head of department while making notes and occasionally discussing them with him. The main objective of ...
- short-paperJune 2018
Development of diagnostic performance & visual processing in different types of radiological expertise
ETRA '18: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & ApplicationsJune 2018, Article No.: 40, Pages 1–6https://doi.org/10.1145/3204493.3204562The aim of this research was to compare visual patterns while examining radiographs in groups of people with different levels and different types of expertise. Introducing the latter comparative base is the original contribution of these studies. The ...
- demonstrationMarch 2018
Medical 3D Images in Multimodal Virtual Reality
IUI '18 Companion: Companion Proceedings of the 23rd International Conference on Intelligent User InterfacesMarch 2018, Article No.: 19, Pages 1–2https://doi.org/10.1145/3180308.3180327We present a multimodal medical 3D image system for radiologists in an virtual reality (VR) environment. Users can walk freely inside the virtual room and interact with the system using speech, going through patient records, and manipulate 3D image data ...
- research-articleMay 2017
Applying trajectory mining in medical image data
PervasiveHealth '17: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for HealthcareMay 2017, Pages 101–109https://doi.org/10.1145/3154862.3154889During evaluation of CT or MR images radiologists navigate through a volume in different orientations in order to detect a disease. While doing so, they leave a trail, which might hold valuable information for other clinicians. Unfortunately, current ...
- research-articleNovember 2016
Artificial neural networks applications in computer aided diagnosis: system design and use as an educational tool
TEEM '16: Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing MulticulturalityNovember 2016, Pages 1201–1208https://doi.org/10.1145/3012430.3012670This paper describes the motivation, state-of-the-art, hypotheses and research objectives of the Doctoral Thesis "Artificial Neural Networks applications in Computer Aided Diagnosis. System design and use as an educational tool". A description of the ...
- research-articleNovember 2016
Computer aided detection and diagnosis in medical imaging: a review of clinical and educational applications
- Jorge Hernández Rodríguez,
- Francisco Javier Cabrero Fraile,
- María José Rodríguez Conde,
- Pablo Luis Gómez Llorente
TEEM '16: Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing MulticulturalityNovember 2016, Pages 517–524https://doi.org/10.1145/3012430.3012567Computer Aided Detection and Diagnosis, known as CAD, is a fundamental tool for assisting radiologists in the image interpretation task. Since the 1980s, its popularity has grown, and it has become an important area of research in computer science, ...
- abstractNovember 2016
Medical imaging VR: can immersive 3D aid in diagnosis?
VRST '16: Proceedings of the 22nd ACM Conference on Virtual Reality Software and TechnologyNovember 2016, Pages 349–350https://doi.org/10.1145/2993369.2996333In the radiology diagnosis process, medical images are most often visualized slice by slice on 2D screens or printed. At the same time, the visualization based on 3D volumetric rendering of the data is considered useful and has increased its field of ...
- research-articleJune 2014
Crafting diversity in radiology image stack scrolling: control and annotations
DIS '14: Proceedings of the 2014 conference on Designing interactive systemsJune 2014, Pages 567–576https://doi.org/10.1145/2598510.2598585To make a single diagnosis, today's radiologists must examine thousands of images; yet little effort has been put into refining this time-consuming, repetitive task. Meanwhile, automatic or radiologist-generated annotations may impact how radiologists ...
- research-articleMay 2014
Pragmatic oriented data interoperability for smart healthcare information systems
CCGRID '14: Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid ComputingMay 2014, Pages 811–818https://doi.org/10.1109/CCGrid.2014.38Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems ...
- ArticleApril 2014
Spreading Relation Annotations in a Lexical Semantic Network Applied to Radiology
CICLing 2014: Proceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing - Volume 8403April 2014, Pages 40–51https://doi.org/10.1007/978-3-642-54906-9_4Domain specific ontologies are invaluable but their development faces many challenges. In most cases, domain knowledge bases are built with very limited scope without considering the benefits of including domain knowledge to a general ontology. ...